Are you preparing for a data science and analytics interview at Ernst and Young (EY)? Congratulations on taking the next step in your career journey! As you gear up for your interview, it’s crucial to be well-prepared for the range of questions that might come your way. To help you ace your interview, we’ve compiled a list of common data science and analytics interview questions along with expert answers tailored for EY’s interview process.
Table of Contents
Technical Interview Questions
Question: Explain the different types of regression models.
Answer:
- Linear Regression: Predicts a continuous variable based on a linear relationship.
- Logistic Regression: Used for binary classification problems.
- Polynomial Regression: Fits a curve to the data with polynomial terms.
- Ridge Regression: Adds a penalty term to linear regression to avoid overfitting.
- Lasso Regression: Similar to Ridge but uses absolute values for a penalty.
- ElasticNet Regression: Combination of Ridge and Lasso, balances their penalties.
Question: What is autocorrelation?
Answer: Autocorrelation is a characteristic of data in which the correlation between the values of the same variables is based on related time periods. It’s essentially the similarity between observations as a function of the time lag between them. In time series data, this can indicate trends, seasonal patterns, or cyclic movements. High autocorrelation might suggest that an underlying process should be incorporated into the model to improve predictions.
Question: What is the difference between tuple and a list?
Answer:
- Mutability: A list is mutable, meaning its elements can be modified after the list has been created. A tuple, however, is immutable, meaning once it’s created, its elements cannot be changed.
- Syntax: Lists are defined with square brackets [], while tuples are defined with parentheses ().
- Performance: Tuples can be slightly faster than lists for certain operations, due to their immutability.
- Usage: Due to their immutability, tuples are often used for data that shouldn’t change, making your code safer and easier to understand. Lists are used for data that is expected to change over time.
Question: What are the Regularization techniques?
Answer: Regularization techniques are methods used to prevent overfitting in machine learning models by adding a penalty on the magnitude of parameters. The main techniques include:
- Lasso (L1 Regularization): Adds an absolute value penalty to the coefficients, leading to feature selection by shrinking some coefficients to zero.
- Ridge (L2 Regularization): Adds a squared value penalty to the coefficients, effectively shrinking them but not necessarily to zero, which helps in reducing model complexity.
- Elastic Net: Combines L1 and L2 regularization penalties, offering a balance between feature selection and coefficient shrinkage, making it useful when dealing with highly correlated data.
Question: Explain the Random forest algorithm.
Answer: The Random Forest algorithm is an ensemble learning method used for both classification and regression tasks. It operates by constructing multiple decision trees during training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random forests aim to reduce overfitting by averaging multiple trees, each built on a random subset of the data and features.
Question: What is Overfitting?
Answer: Overfitting happens when a model learns the noise and details of the training data too well, making it perform poorly on new, unseen data. Essentially, the model memorizes the training examples instead of learning general patterns. It results in high accuracy on training data but low performance on new data. Regularization methods are used to prevent overfitting by controlling the complexity of the model.
Question: How Support Vector Machine is defined?
Answer: A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that best separates the classes in a high-dimensional space. The hyperplane is chosen to maximize the margin between the closest data points of different classes, known as support vectors. SVM is effective in handling both linear and non-linear data using different kernel functions like linear, polynomial, and radial basis functions (RBF).
Machine Learning Interview Questions
Question: What is the difference between supervised and unsupervised learning?
Answer: Supervised learning involves training a model on labeled data, where the algorithm learns to map input to output based on example input-output pairs. Unsupervised learning deals with unlabeled data, where the algorithm tries to find patterns and relationships without explicit guidance.
Question: Explain the bias-variance tradeoff.
Answer: The bias-variance tradeoff refers to the balance between a model’s ability to capture the underlying patterns in the data (low bias) and its sensitivity to noise and fluctuations (low variance). A model with high bias may oversimplify the data, while a high-variance model may overfit the noise.
Question: What is cross-validation and why is it important?
Answer: Cross-validation is a technique used to assess the performance and generalization ability of a model. It involves dividing the data into multiple subsets, training the model on some of these subsets, and testing it on the remaining subset. This helps in estimating how the model will perform on unseen data.
Question: Describe the process of feature selection.
Answer: Feature selection involves choosing the most relevant and informative features from the dataset to use in building a model. This helps in improving model performance, reducing overfitting, and enhancing interpretability.
Question: What is the purpose of regularization in machine learning?
Answer: Regularization is used to prevent overfitting by adding a penalty term to the model’s objective function. It helps in controlling the complexity of the model and discourages overly complex models that may perform well on training data but poorly on new data.
Question: Explain the Random Forest algorithm.
Answer: Random Forest is an ensemble learning method that builds multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. It reduces overfitting by averaging the predictions of the trees, each trained on a random subset of data and features.
Question: What is the difference between precision and recall?
Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, focusing on the accuracy of positive predictions. Recall is the ratio of correctly predicted positive observations to all actual positives, emphasizing the ability of the model to find all the positive samples.
Question: How does a Support Vector Machine (SVM) work?
Answer: SVM is a supervised learning algorithm used for classification tasks. It finds the optimal hyperplane that best separates the classes in a high-dimensional space. The hyperplane is chosen to maximize the margin between the closest data points of different classes, known as support vectors.
Statistics and Probability Interview Questions
Question: What is the Central Limit Theorem?
Answer: The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. This is important because it allows us to make inferences about a population mean from a sample mean.
Question: Explain the difference between population and sample.
Answer: A population is the entire group of individuals or items that we are interested in studying, while a sample is a subset of the population that is observed or studied. The goal of statistical inference is to make conclusions about the population based on the information from the sample.
Question: What is the p-value in hypothesis testing?
Answer: The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the one observed in the sample data, assuming the null hypothesis is true. It is used to determine the statistical significance of our results. A low p-value (usually below 0.05) indicates that we have evidence to reject the null hypothesis.
Question: Describe the difference between Type I and Type II errors.
Answer: Type I error (false positive) occurs when we reject a null hypothesis that is true. Type II error (false negative) occurs when we fail to reject a null hypothesis that is false. The significance level (alpha) of a hypothesis test affects the likelihood of Type I errors, while the power of a test affects the likelihood of Type II errors.
Question: Explain the concept of correlation.
Answer: Correlation measures the strength and direction of a linear relationship between two continuous variables. It ranges from -1 to +1, where -1 indicates a perfect negative linear relationship, +1 indicates a perfect positive linear relationship, and 0 indicates no linear relationship.
Question: What is Bayesian probability?
Answer: Bayesian probability is a way of quantifying uncertainty by assigning probabilities to events based on prior knowledge and evidence. It involves updating probabilities as new evidence becomes available, using Bayes’ theorem. This approach is particularly useful in cases where we have prior beliefs or information about the events.
Question: Explain the difference between probability and odds.
Answer: Probability is the likelihood of an event occurring and is expressed as a number between 0 and 1. Odds, on the other hand, represent the ratio of the probability of success to the probability of failure. They can be expressed as odds in favor (successes to failures) or odds against (failures to successes).
Question: What is a confidence interval?
Answer: A confidence interval is a range of values around a sample estimate (such as a mean or proportion) that is likely to contain the true population parameter with a certain level of confidence. For example, a 95% confidence interval means that we are 95% confident that the true parameter lies within the interval.
SQL and R Interview Questions
Question: What is SQL and what are its main uses?
Answer: SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. It is used for tasks such as querying data, updating data, creating and modifying database schemas, and managing database access.
Question: Explain the difference between INNER JOIN and LEFT JOIN.
Answer: INNER JOIN returns rows when there is at least one match in both tables based on the join condition, while LEFT JOIN returns all rows from the left table (the first table mentioned in the query) and the matched rows from the right table (the second table mentioned).
Question: What is the difference between WHERE and HAVING clauses in SQL?
Answer: The WHERE clause is used to filter rows before they are grouped and aggregated, while the HAVING clause is used to filter groups after they have been formed by the GROUP BY clause.
Question: How do you remove duplicates from a table in SQL?
Answer: To remove duplicates from a table, you can use the DISTINCT keyword in a SELECT query or use the GROUP BY clause with appropriate columns.
Question: Explain the concept of NULL in SQL.
Answer: NULL represents missing or unknown values in SQL. It is not the same as zero or an empty string. Operations involving NULL usually result in NULL unless specifically handled using functions like IS NULL or COALESCE.
Question: What is R and why is it used in data analysis?
Answer: R is a programming language and environment specifically designed for statistical computing and data analysis. It provides a wide range of statistical and graphical techniques and is widely used in academia and industry for data analysis and visualization.
Question: Explain the difference between a vector and a list in R.
Answer: A vector in R is a one-dimensional array that can hold elements of the same data type, while a list is a collection of elements of different data types. Lists can contain vectors, matrices, data frames, and other lists.
Question: How do you read data from a CSV file into R?
You can use the read.csv() function in R to read data from a CSV file. For example: my_data <- read.csv(“file_path/my_data.csv”)
Question: Explain what a data frame is in R.
Answer: A data frame in R is a two-dimensional data structure that is similar to a table in a database or a spreadsheet. It consists of rows and columns, where each column can be of a different data type.
Technical Interview Questions
- ML Model building life cycle
- Some other statistical questions.
- Question on probability and statistics,
- Data mining algorithm
- Python programming,
- Questions on SQL
- Deep learning,
- NLP
- Tableau
- R language
Other Interview Questions
Que: How do you understand Machine Learning Techniques
Que: Formulae for sigmoid functions and
Que: What was one of the biggest data sets you mined for info?
Que: What do you currently do as a data scientist?
Behavioral Interview Questions
Que: Tell me about yourself.
Que: Why Do You Want to Work For EY?
Que: Why should we hire you?
Que: What is your current salary?
Que: What is your salary expectation?
Conclusion
Preparing for a data science and analytics interview at Ernst and Young (EY) requires a solid grasp of fundamental concepts in data science, machine learning, SQL, R programming, and statistical analysis. By familiarizing yourself with these common interview questions and expert answers, you’ll be better equipped to showcase your skills, problem-solving abilities, and analytical thinking.
Remember to also emphasize your ability to communicate complex ideas, collaborate in team settings, and adapt to challenging projects. Good luck on your interview journey at Ernst and Young (EY) as you embark on an exciting career in data science and analytics!
